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J Mol Graph Model. 2013 Jul;44:278-85. doi: 10.1016/j.jmgm.2013.07.005. Epub 2013 Jul 23.

A combined molecular docking-based and pharmacophore-based target prediction strategy with a probabilistic fusion method for target ranking.

Author information

1
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, China.

Abstract

Herein, a combined molecular docking-based and pharmacophore-based target prediction strategy is presented, in which a probabilistic fusion method is suggested for target ranking. Establishment and validation of the combined strategy are described. A target database, termed TargetDB, was firstly constructed, which contains 1105 drug targets. Based on TargetDB, the molecular docking-based target prediction and pharmacophore-based target prediction protocols were established. A probabilistic fusion method was then developed by constructing probability assignment curves (PACs) against a set of selected targets. Finally the workflow for the combined molecular docking-based and pharmacophore-based target prediction strategy was established. Evaluations of the performance of the combined strategy were carried out against a set of structurally different single-target compounds and a well-known multi-target drug, 4H-tamoxifen, which results showed that the combined strategy consistently outperformed the sole use of docking-based and pharmacophore-based methods. Overall, this investigation provides a possible way for improving the accuracy of in silico target prediction and a method for target ranking.

KEYWORDS:

A probabilistic fusion method; Drug target prediction; Molecular docking; Pharmacophore

PMID:
23933279
DOI:
10.1016/j.jmgm.2013.07.005
[Indexed for MEDLINE]

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